Diagnostic Innovations in Glaucoma Study (DIGS): Glaucoma and High Myopia

青光眼研究 (DIGS) 的诊断创新：青光眼和高度近视

基本信息

批准号：
10686341
负责人：
LINDA M ZANGWILL
金额：
$ 66.75万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-03-01 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10686341
关键词：
3-Dimensional Address Age Aging American Society of Clinical Oncology Anterior Atrophic Biomechanics Blindness Blood Vessels Bruch&apos s basal membrane structure Clinical Cohort Studies Communities Complex Data Databases Detection Diagnosis Diagnostic Disease Disease Management Dropout Eye Functional disorder Glaucoma Goals High Prevalence Image Individual Inner Plexiform Layer Japan Korea Lead Length Link Maps Measures Modeling Morphology Myopia Open-Angle Glaucoma Optic Disk Optic Nerve Optical Coherence Tomography Papillary Patients Pattern Persons Phenotype Population Predisposition Prevalence Research Retina Risk Scanning Science Scotoma Secure Statistical Methods Testing Texture Thick Thinness Vision research Visual Fields clinical biomarkers clinical care cloud based cohort data curation data pipeline data sharing deep learning deep learning algorithm deep learning model density design follow-up functional disability functional loss ganglion cell high risk improved innovation learning strategy macula model development multimodality novel patient oriented predictive modeling prevent retinal nerve fiber layer vascular factor

项目摘要

Project Summary The objective of this study, “Diagnostic Innovations in Glaucoma Study (DIGS): Glaucoma and High Myopia”, is to overcome barriers to the detection of open angle glaucoma (OAG) in individuals with high myopia (mypOAG). In 2010, there was an estimated 1.4 billion people worldwide with myopia and the prevalence is rapidly rising to an estimated 4.75 billion by 2050. Moreover, persons with high myopia are 2.5 times more likely to have OAG than those without high myopia. It is unclear why myopia increases the risk of OAG, but it is likely related at least in part to biomechanical factors; longer axial lengths in myopic eyes may result in deformation of the lamina cribrosa, temporal displacement of Bruch's membrane, parapapillary changes and vascular factors; these all lead to increased susceptibility of the optic nerve to OAG damage. Given the higher prevalence of tilted discs and peripapillary atrophy in myopic eyes, the structural and functional tests that usually guide treatment decisions are of diminished value. This proposal will provide essential follow-up to establish best practices for patient-centered detection of OAG progression in the challenging high myopia population. Specifically, this proposal will 1) identify optic nerve head (ONH) 3D morphologic parameters from optical coherence tomography (OCT) scans (segmented and unsegmented) to differentiate between myopia eyes with and without progressive OAG; 2) optimize change detection using novel OCT features (e.g. texture and microvasculature) from wide field of view (WFOV) maps merged from individual ONH and macula scans; and 3) develop novel longitudinal and multimodal deep learning (DL) models to predict OAG progression. Most importantly, we will improve our understanding of the complex temporal relationship between structural, functional and microvascular age- and OAG related changes in a diverse cohort across the range of myopia. Specifically, in Specific Aim 1 (To improve our understanding of the complex relationship between ONH morphology and structural, functional, and microvascular change in the aging and OAG eye), we address several hypotheses related to the characterization of myopic ONH morphology in healthy eyes with and without high myopia. We hypothesize that ONH morphology is predictive of age – and OAG related structural, functional and microvascular changes and that it is predictive of fast progression. In Specific Aim 2 (To improve detection of OAG progression in myopic eyes using WFOV maps, unsegmented 3D volumes, ONH morphology), we address several hypotheses designed to detect and predict OAG progression using novel DL approaches. In Specific Aim 3, we will establish a cloud-based pipeline for data curation and computation that will facilitate secure DL model development and extensive data sharing with the vision research community.

项目概要这项研究的目标“青光眼研究 (DIGS) 的诊断创新：青光眼和高度近视”，旨在克服高度近视个体开角型青光眼 (OAG) 检测的障碍 (mypOAG)，2010 年，全球估计有 14 亿人患有近视，患病率是预计到 2050 年，这一数字将迅速上升至 47.5 亿。此外，高度近视人数将增加 2.5 倍与没有高度近视的人相比，患有 OAG 的可能性更高目前还不清楚为什么近视会增加 OAG 的风险，但确实如此。可能至少部分与生物力学因素有关；近视眼的眼轴较长可能会导致筛板变形、布鲁赫膜暂时移位、视乳头旁变化和血管因素；这些都会导致视神经对 OAG 损伤的敏感性增加。近视眼中椎间盘倾斜和视乳头周围萎缩的患病率，结构和功能测试通常指导治疗决策的价值会降低。建立以患者为中心的高度近视 OAG 进展检测最佳实践具体来说，该提案将 1) 识别视神经乳头 (ONH) 3D 形态参数。光学相干断层扫描 (OCT) 扫描（分段和非分段）以区分近视具有和不具有渐进式 OAG 的眼睛；2) 使用新颖的 OCT 特征（例如纹理）优化变化检测和微脉管系统）来自于单个 ONH 和黄斑扫描合并的宽视野 (WFOV) 图； 3) 开发新颖的纵向和多模态深度学习 (DL) 模型来预测 OAG 进展。重要的是，我们将提高对结构、不同近视范围的不同队列中功能和微血管年龄和 OAG 相关的变化。具体来说，在具体目标 1 中（提高我们对 ONH 之间复杂关系的理解）老化和 OAG 眼的形态和结构、功能和微血管变化），我们解决与健康眼近视 ONH 形态特征相关的几个假设我们认为 ONH 形态可以预测年龄以及 OAG 相关的结构，功能和微血管变化，并且它可以预测快速进展。具体目标 2（改善）使用 WFOV 图、未分段 3D 体积、ONH 检测近视眼的 OAG 进展形态学），我们提出了几个旨在使用新型 DL 检测和预测 OAG 进展的假设在具体目标 3 中，我们将建立一个基于云的数据管理和计算管道。将促进安全的深度学习模型开发以及与视觉研究社区的广泛数据共享。